基于GPU的线性代数快速DNA混合分析方法
A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs
摘要：
DNA样本分析是法医学的重要步骤，分析速度可能会影响调查。DNA序列的比较是基于对短串联重复序列（STR）的分析，STR是2-5个碱基对的短DNA序列。目前的法医学方法使用20个STR基因座进行分析。单核苷酸多态性（SNP）的使用对于复杂DNA混合物的分析具有实用性。使用数万个SNP基因座进行分析带来了重大的计算挑战，因为法医分析通过基因座计数和待分析的DNA样品数量的乘积进行缩放。在本文中，我们讨论了实现的DNA序列比较算法，通过重新铸造的线性代数原语的算法。通过开发一种用于DNA比较的重载矩阵乘法方法，我们可以利用GPU硬件和密集广义矩阵乘法（DGEMM）算法的进步来加速DNA样本比较。我们证明，使用NVIDIA K80 GPU可以在6秒内将2048个未知DNA样本与2000万个已知样本进行比较。
英文摘要：
Analysis of DNA samples is an important step in forensics, and the speed of analysis can impact investigations. Comparison of DNA sequences is based on the analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5 base pairs. Current forensics approaches use 20 STR loci for analysis. The use of single nucleotide polymorphisms (SNPs) has utility for analysis of complex DNA mixtures. The use of tens of thousands of SNPs loci for analysis poses significant computational challenges because the forensic analysis scales by the product of the loci count and number of DNA samples to be analyzed. In this paper, we discuss the implementation of a DNA sequence comparison algorithm by re-casting the algorithm in terms of linear algebra primitives. By developing an overloaded matrix multiplication approach to DNA comparisons, we can leverage advances in GPU hardware and algoithms for Dense Generalized Matrix-Multiply (DGEMM) to speed up DNA sample comparisons. We show that it is possible to compare 2048 unknown DNA samples with 20 million known samples in under 6 seconds using a NVIDIA K80 GPU.
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